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SQL Data Science
Return to Data Science, Machine Learning - Deep Learning, SQL Machine Learning, DataOps-MLOps-DevOps, SQL Official Glossary, SQL Topics, SQL, SQL DevOps - SQL SRE, SQL DataOps, SQL MLOps
- Snippet from Wikipedia: Data science
Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data.
Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.
Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.
A data scientist is a professional who creates programming code and combines it with statistical knowledge to create insights from data.
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SQL: SQL Fundamentals, SQL Inventor - SQL Language Designer: Donald D. Chamberlin and Raymond F. Boyce from IBM San Jose Research Laboratory in 1974 after learning about the relational model from Edgar F. Codd; SQL DevOps - SQL SRE, Cloud Native SQL (SQL on Kubernetes - SQL on AWS - SQL on Azure - SQL on GCP - SQL on Mainframe), SQL Microservices, SQL Containerization (SQL Docker - SQL on Docker Hub), Serverless SQL, SQL Data Science - SQL DataOps - SQL and Databases (SQL ORM), SQL ML - SQL DL, Database - Database Fundamentals, Relational Databases (Oracle Database, MySQL, SQL Server (T-SQL - Transact-SQL), PostgreSQL, IBM Db2, Azure SQL Database, Snowflake, Google BigQuery, Google BigTable, SQLite), Functional SQL (1. SQL Immutability, 2. SQL Purity - SQL No Side-Effects, 3. SQL First-Class Functions - SQL Higher-Order Functions, SQL Lambdas - SQL Anonymous Functions - SQL Closures, SQL Lazy Evaluation, 4. SQL Recursion), Reactive SQL), SQL Concurrency SQL and ACID - SQL Parallel Programming - Async SQL, SQL Networking, SQL Security - SQL DevSecOps - SQL OAuth, SQL Memory Allocation (SQL Heap - SQL Stack - SQL Garbage Collection), SQL CI/CD - SQL Dependency Management - SQL DI - SQL IoC - SQL Build Pipeline, SQL Automation - SQL Scripting, SQL Package Managers, SQL Modules - SQL Packages, SQL Installation (SQL Windows - Chocolatey SQL, SQL macOS - Homebrew SQL, SQL on Linux), SQL Configuration, SQL Observability (SQL Monitoring, SQL Performance - SQL Logging), SQL Language Spec - SQL RFCs - SQL Roadmap, SQL Keywords, SQL Operators, SQL Functions, SQL Data Structures - SQL Algorithms, SQL Syntax, SQL OOP (1. SQL Encapsulation - 2. SQL Inheritance - 3. SQL Polymorphism - 4. SQL Abstraction), SQL Design Patterns - SQL Best Practices - SQL Style Guide - Clean SQL - SQL BDD, SQL Generics, SQL I/O, SQL Serialization - SQL Deserialization, SQL APIs, SQL REST - SQL JSON - SQL GraphQL, SQL gRPC, SQL Virtualization, SQL Development Tools: SQL SDK, SQL Compiler - SQL Transpiler, SQL Interpreter - SQL REPL, SQL IDEs - Database IDEs (JetBrains DataSpell, SQL Server Management Studio, MySQL Workbench, Oracle SQL Developer, SQLiteStudio, JetBrains SQL, SQL Visual Studio Code), SQL Linter, SQL Community - SQLaceans - SQL User, SQL Standard Library - SQL Libraries - SQL Frameworks, SQL Testing - SQL TDD, SQL History, SQL Research, SQL Topics, SQL Uses - List of SQL Software - Written in SQL - SQL Popularity, SQL Bibliography - Manning SQL Series - Manning Data Science Series - SQL Courses, SQL Glossary - SQL Official Glossary, SQL GitHub, Awesome SQL. (navbar_sql and navbar_database)
Data Science: Fundamentals of Data Science, DataOps, Big Data, Data Science IDEs (Jupyter Notebook, JetBrains DataGrip, Google Colab, JetBrains DataSpell, SQL Server Management Studio, MySQL Workbench, Oracle SQL Developer, SQLiteStudio), Data Science Tools (SQL, Apache Arrow, Pandas, NumPy, Dask, Spark, Kafka); Data Science Programming Languages (Python Data Science, NumPy Data Science, R Data Science, Java Data Science, C++ Data Science, MATLAB Data Science, Scala Data Science, Julia Data Science, Excel Data Science (Excel is the most popular "programming language") - Google Sheets, SAS Data Science, C# Data Science, Golang Data Science, JavaScript Data Science, Kotlin Data Science, Ruby Data Science, Rust Data Science, Swift Data Science, TypeScript Data Science, Bash Data Science); Databases, Data, Augmentation, Analysis, Analytics, Archaeology, Cleansing, Collection, Compression, Corruption, Curation, Degradation, Editing (EmEditor), Data engineering, ETL/ ELT ( Extract- Transform- Load), Farming, Format management, Fusion, Integration, Integrity, Lake, Library, Loss, Management, Migration, Mining, Pre-processing, Preservation, Protection (privacy), Recovery, Reduction, Retention, Quality, Science, Scraping, Scrubbing, Security, Stewardship, Storage, Validation, Warehouse, Wrangling/munging. ML-DL - MLOps. Data science history, Data Science Bibliography, Manning Data Science Series, Data science Glossary, Data science topics, Data science courses, Data science libraries, Data science frameworks, Data science GitHub, Data Science Awesome list. (navbar_datascience - see also navbar_python, navbar_numpy, navbar_data_engineering and navbar_database)
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